Chili-Net: An Approach for Classifying Chili Leaf Diseases Using Deep Neural Networks

Chili plants face susceptibility to a range of fungal, bacterial, and viral diseases, with leaves being particularly vulnerable. This study focuses on chili leaves and provides an extensive examination of various chili diseases and existing research in this domain. Traditional disease detection methods are labor-intensive for farmers. The paper introduces innovative image processing and deep learning techniques for early and effective disease identification in chili leaves. The proposed framework highlights a well-defined methodology to classify chili leaf diseases from the healthy ones termed as Chili-Net. The work focuses on an ensemble technique to extract features from the images. Three different deep neural networks viz. ResNet 50, VGG 16 and Inception V3 are integrated into a single channel to extract important features. Furthermost a customized deep neural network comprising 10 layers is utilized to classify the classes. The presented framework generated an accuracy of 97%, precision of 100% and AUC Score of 100% demonstrating the efficacy of the proposed architecture.